A new statistical technique developed by a researcher at the Texas A&M University School of Public Health and colleagues elsewhere offers fresh insights into how diseases affect individual cells. This innovative method, known as hybrid Bayesian inference, blends different statistical approaches to better understand complex diseases like idiopathic pulmonary fibrosis, which has long puzzled scientists due to its elusive nature.

The technique was developed over the past decade by Gang Han, Ph.D., and others for use when prior information is available for some parameters but not others. This approach paves the way for more precise medical discoveries in many areas.

"This hybrid approach is better than the frequentist inference because it includes prior information and also better than the Bayesian inference because it reduces the problem of bias resulting from noninformative priors, which can be significant with small sample sizes," said Han, who is a professor at the School of Public Health.

The current single-cell RNA sequencing technology faces the same challenge when it comes to identifying the genes of interest. Differentially expressed genes can be identified by pooling single-cell RNA sequencing into the same biological replicates, but the small sample size reduces their power. On the other hand, acquiring large sample sizes can hardly be possible due to high cost and prolonged accrual of patients with certain diseases.

For their current study in Human Genomics, Han and colleagues from the pharmaceutical company Eli Lilly and Company applied the Bayesian-frequentist hybrid framework to a case study involving idiopathic pulmonary fibrosis. Using a semisynthetic data source of single-cell RNA sequencing of mouse hypothalamus, the researchers studied the statistical power and false discovery rate of the Bayesian frequentist hybrid inference compared to other analysis methods.

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